1. What Are Parameters? This was covered in a previous issue: What Are Parameters? Why Are “Bigger” Models Often “Smarter”? 2. The Relationship Between Parameter Count and Inference Speed As the number of parameters in a model increases, it requires more computational resources to perform inference (i.e., generate results). This directly impacts inference speed. However, the relationship between parameters and speed is not a straightforward inverse correlation. Several factors influence inference speed: (1) Computational Load (FLOPs) The number of floating-point operations (FLOPs) required by a model directly impacts inference time. However, FLOPs are not the sole determinant since different types of operations may execute with varying efficiency on hardware. (2) Memory Access Cost During inference, the model frequently accesses memory. The volume of memory access (or memory bandwidth requirements) can affect speed. For instance, both the computational load and memory access demands of deep learning models significantly impact deployment and inference performance. (3) Model Architecture The design of the model, including its parallelism and branching structure, influences efficiency. For example, branched architectures may introduce synchronization overhead, causing some compute units to idle and slowing inference. (4) Hardware Architecture Different hardware setups handle models differently. A device’s computational power, memory bandwidth, and overall architecture all affect inference speed. Efficient neural network designs must balance computational load and memory demands for optimal performance across various hardware environments. Thus, while parameter count is one factor affecting inference time, it’s not a simple inverse relationship. Optimizing inference speed requires consideration of computational load, memory access patterns, model architecture, and hardware capabilities. 3. Why Are…

1. What is Prompt Engineering? Prompt Engineering is a core technique in the field of generative AI. Simply put, it involves crafting effective input prompts to guide AI in producing the desired results. Generative AI models (like GPT-3 and GPT-4) are essentially predictive tools that generate outputs based on input prompts. The goal of Prompt Engineering is to optimize these inputs to ensure that the AI performs tasks according to user expectations. Here’s an example: Input: “Explain quantum mechanics in one sentence.” Output: “Quantum mechanics is a branch of physics that studies the behavior of microscopic particles.” The quality of the prompt directly impacts AI performance. A clear and targeted prompt can significantly improve the results generated by the model. 2. Why is Prompt Engineering important? The effectiveness of generative AI depends heavily on how users present their questions or tasks. The importance of Prompt Engineering can be seen in the following aspects: (1) Improving output quality A well-designed prompt reduces the risk of the AI generating incorrect or irrelevant responses. For example: Ineffective Prompt: “Write an article about climate change.” Optimized Prompt: “Write a brief 200-word report on the impact of climate change on the Arctic ecosystem.” (2) Saving time and cost A clear prompt minimizes trial and error, improving efficiency, especially in scenarios requiring large-scale outputs (e.g., generating code or marketing content). (3) Expanding AI’s use cases With clever prompt design, users can leverage AI for diverse and complex tasks, from answering questions to crafting poetry, generating code, or even performing data analysis. 3. Core techniques in Prompt…

1. What Are Parameters? In deep learning, parameters are the trainable components of a model, such as weights and biases, which determine how the model responds to input data. These parameters adjust during training to minimize errors and optimize the model's performance. Parameter count refers to the total number of such weights and biases in a model. Think of parameters as the “brain capacity” of an AI model. The more parameters it has, the more information it can store and process. For example: A simple linear regression model might only have a few parameters, such as weights ( ww  w) and a bias ( bb  b). GPT-3, a massive language model, boasts 175 billion parameters, requiring immense computational resources and data to train. 2. The Relationship Between Parameter Count and Model Performance In deep learning, there is often a positive correlation between a model's parameter count and its performance. This phenomenon is summarized by Scaling Laws, which show that as parameters, data, and computational resources increase, so does the model's ability to perform complex tasks. Why Are Bigger Models Often Smarter? Higher Expressive Power Larger models can capture more complex patterns and features in data. For instance, they not only grasp basic grammar but also understand deep semantic and contextual nuances. Stronger Generalization With sufficient training data, larger models generalize better to unseen scenarios, such as answering novel questions or reasoning about unfamiliar topics. Versatility Bigger models can handle multiple tasks with minimal or no additional training. For example, OpenAI's GPT models excel in creative writing, code generation, translation, and…

"Self Attention", a pivotal advancement in deep learning, is at the core of the Transformer architecture, revolutionizing how models process and understand sequences. Unlike traditional Attention, which focuses on mapping relationships between separate input and output sequences, Self-Attention enables each element within a sequence to interact dynamically with every other element. This mechanism allows AI models to capture long-range dependencies more effectively than previous architectures like RNNs and LSTMs. By computing relevance scores between words in a sentence, Self-Attention ensures that key relationships—such as pronoun references or contextual meanings—are accurately identified, leading to more sophisticated language understanding and generation. 1. The Origin of the Attention Mechanism The Attention Mechanism is one of the most transformative innovations in deep learning. First introduced in the 2014 paper Neural Machine Translation by Jointly Learning to Align and Translate, it was designed to address a critical challenge: how can a model effectively focus on the most relevant parts of input data, especially in tasks involving long sequences? Simply put, the Attention Mechanism allows models to “prioritize,” much like humans skip unimportant details when reading and focus on the key elements. This breakthrough marks a shift in AI from rote memorization to dynamic understanding. 2. The Core Idea Behind the Attention Mechanism The Attention Mechanism’s main idea is simple yet powerful: it enables the model to assign different levels of importance to different parts of the input data. Each part of the sequence is assigned a weight, with higher weights indicating greater relevance to the task at hand. For example, when translating the sentence “I…

1. What is the Transformer? The Transformer is a deep learning architecture introduced by Google Research in 2017 through the seminal paper Attention is All You Need. Originally designed to tackle challenges in natural language processing (NLP), it has since transformed into the foundation for state-of-the-art AI models in multiple domains, such as computer vision, speech processing, and multimodal learning. Traditional NLP models like RNNs (Recurrent Neural Networks) and LSTMs (Long Short-Term Memory networks) had two significant shortcomings: Sequential Processing: These models processed text one token at a time, slowing down computations and making it hard to parallelize. Difficulty Capturing Long-Range Dependencies: For long sentences or documents, these models often lost crucial contextual information from earlier parts of the input. The Transformer introduced a novel Self-Attention Mechanism, enabling it to process entire input sequences simultaneously and focus dynamically on the most relevant parts of the sequence. Think of it like giving the model a panoramic lens, allowing it to view the entire context at once, rather than just focusing on one word at a time. 2. Why is the Transformer Important? The Transformer brought a paradigm shift to AI, fundamentally altering how models process, understand, and generate information. Here's why it’s considered revolutionary: (1) Parallel Processing Unlike RNNs that process data step by step, Transformers can analyze all parts of the input sequence simultaneously. This parallelism significantly speeds up training and inference, making it feasible to train models on massive datasets. (2) Better Understanding of Context The Self-Attention Mechanism enables the Transformer to capture relationships between all tokens in a…

The Problem: Too Much Dust on Old Photos, I need "Remove Duplicate Photos" cleaner Imagine sifting through tens of thousands of photos—manually. I mounted the NAS SMB partition on my MacBook, only to discover it was excruciatingly slow. After two days of copying files to my MacBook, my manual review session turned into a blur. My eyes hurt, my patience wore thin, and I knew there had to be a better way. When I turned to existing tools for "remove duplicate photo" task, I hit a wall. Most were paid, overly complex, or simply didn’t fit my needs. Even the so-called free solutions required learning arcane commands like find. I needed something powerful, flexible, and fast. And when all else fails, what’s a tech enthusiast to do? Write their own solution—with a "little" help from ChatGPT. The Power of ChatGPT I’d dabbled with the same task scripting years ago but quickly gave up because of the time it required. Enter ChatGPT (no marketing here... I am a paid user though...), the real hero of this story. With its assistance, I wrote the majority of the script in less than a day before i gave up ! But anyway, of course, I still have to thank the emergence of Large Language Models! Based on the current code volume and quality, without 10 to 15 days, a single person would absolutely not be able to achieve the current results! So, I believe LLMs have helped me improve my efficiency by at least 10 times! And they've helped me avoid all sorts of…

So, I did a thing. I earned my first Microsoft certificate: Azure AI Engineer Associate! 🎉 Here is my story from training to passing the AI-102 exam. The Learning Journey of AI-102 The journey began with a four-and-a-half-day company-provided AI-102 training session. It was a mix of online classes and labs. I was actually on vacation during this period, so I only managed to focus for about three days, probably. The labs provided during the training were very useful. There were about 10 labs, each lab could be done up to 10 times, with each session lasting 1 to 3 hours. So I didn’t need to pay Microsoft to get familiar with the Azure AI environment. Roughly calculation, the training provides 100 to 200 hours of lab time available, but I only used about 20 hours before taking the exam. After the AI-102 training, I mainly sticked to Microsoft Learn: Designing and Implementing a Microsoft Azure AI Solution to fill gaps. Trust me, that's really helpful! The MS Learn modules helped me understand the concepts better. Cramming and Building Knowledge for AI-102 As the exam date got closer, I quickly skimmed John Savill’s Technical Training videos on YouTube for one time. His videos helped me build a complete knowledge framework in my head. One time is enough for me. Last but not least, please do read Areeb Pasha's AI-102 notes on Notion! Thanks to Areeb Pasha! It's so useful. These notes were like a concise version of MS Learn and made my studying very efficient. I managed to cover all…

Introduction Are you ready to delve into the exciting realm of Azure AI? Whether you're a seasoned developer or just starting your journey in the world of artificial intelligence, Microsoft Build offers a transformative opportunity to harness the power of AI. Recently I came across several good tutorials on Microsoft website, e.g. "CLOUD SKILLS CHALLENGE: Microsoft Build: Build multimodal Generative AI experiences". I enjoyed the learning on it. But I found out the very first step many people might seem as a challange: get az command work on Mac! So I decided to write down all my fix. Let's go! Resolution I am following up "Install Azure on Mac". Run command: But it failed with permission issue on openssl package: I fixed it by changing the permission of /usr/local/lib to current user, but it's not enough. I hit Python permission issue at a different location: So I had to apply the permission to /usr/local. So the command is: The screenshot of brew unlink command: Finally it finished installation successfully! Well done! Ps. You're welcome to access my other AI Insights blog posts at here.

I finished the course! I really enjoyed the learning experiences in Andrew's course so far. Let's see what I've learn for the two days! Overfitting - The Last Topic of this Course! Overfitting It occurs when a machine learning model learns the details and noise in the training data to an extent that it negatively impacts the performance of the model on new data. This means the model is great at predicting or fitting the training data but performs poorly on unseen data, due to its inability to generalize from the training set to the broader population of data. The course explains that overfitting can be addressed by: We can't bypass underfitting. Overfitting and underfitting both are undesirable effects that suggest a model is not well-tuned to the task at hand, but they stem from opposite causes and have different solutions. Below two screenshots captured from course for my notes: Questions help me to master the content Words From Andrew At The End! I want to say congratulations on how far you've come and I want to say great job for getting through all the way to the end of this video.  I hope you also work through the practice labs and quizzes.  Having said that, there are still many more exciting things to learn. Awesome! I am already ready for next machine learning journeys!

Let's continue! Today is mainly learning about "Decision boundary", "Cost function of logistic regresion", "Logistic loss" and "Gradient Descent Implementation for logistic regression". We found out the "Decision boundary" is when z equals to 0 in the sigmod function. Because at this moment, its value will be just at neutral position. Andrew gave an example with two variables, x1 + x2 - 3 (w1 = w2 = 1) the decision bounday is the line of x1 + x2 = 3. I want to say "Cost function for logistic regression" is the most hard in week 3 so far I've seen. I haven't quite figured out why the square error cost function not applicable and where the loss function came from. I have to re-watch the videos again. The lab is also useful. This particular cost function in above is derived from statistics using a statistical principle called maximum likelihood estimation (MLE).     Questions and Answers Some thoughts of today Honestly, it feels like it's getting tougher and tougher. I can still get through the equations and derivations alright, it’s just that as I age, I feel like my brain is just not keeping up. At the end of each video, Andrew always congratulates me with a big smile, saying I’ve mastered the content of the session. But deep down, I really think what he's actually thinking is, "Ha, got you stumped again!" However, to be fair, Andrew really does explain things superbly well. I hope someday I can truly master this knowledge and use it effortlessly. Fighting! Ps. Feel free to…